Alteration-free and Model-agnostic Origin Attribution of Generated Images

• URL: http://arxiv.org/abs/2305.18439v1
• Date: Mon, 29 May 2023 01:35:37 GMT
• Title: Alteration-free and Model-agnostic Origin Attribution of Generated Images
• Authors: Zhenting Wang, Chen Chen, Yi Zeng, Lingjuan Lyu, Shiqing Ma
• Abstract summary: Concerns have emerged regarding potential misuse of image generation models. It is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model.
• Score: 28.34437698362946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement associated with these models. Therefore, it is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods are limited in their applicability to specific types of generative models and require additional steps during training or generation. This restricts their use with pre-trained models that lack these specific operations and may compromise the quality of image generation. To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for the generated images of the given model and other images. Based on our analysis, we propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images from a specific generative model and other images, including those generated by different models and real images.

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